0) Setup
#libzzs---
library(ggplot2)
library(dplyr)
library(ggpubr)
library(car)
library(emmeans)
library(report)
library(DHARMa)
rm(list=ls())
set.seed(666) #Fix kernell
1) Import data & affect factors
setwd("/Users/martinmartin/Downloads/DATA/Github/Field")
sAll<-read.table("Field_G_A_P_activity.csv", sep="",header=T)
sA1<-sAll %>% filter(cond=="GC"|cond=="PC") %>% mutate(cond="CC")
sA2<-sAll %>% filter(cond!="GC"&cond!="PC")
sAll<-bind_rows(sA1,sA2)
sAll$cat<-as.factor(sAll$cat)
sAll$ID<-as.factor(sAll$ID)
sAll$cond<-as.factor(sAll$cond)
sAll$expe<-as.factor(sAll$expe)
levels(sAll$cond)
## [1] "A-" "A+" "AA" "CC" "G-" "G+" "P-" "P+"
c1<-"A 200\u00b5g/L"
c2<-"A 500\u00b5g/L"
c3<-"Control N°1"
c4<-"Control N°2"
c5<-"G 100\u00b5g/L"
c6<-"G 200\u00b5g/L"
c7<-"P 1mg/L"
c8<-"P 10mg/L"
levels(sAll$cond) <- c(c1,c2,c3,c4,c5,c6,c7,c8)
levels(sAll$cond)
## [1] "A 200µg/L" "A 500µg/L" "Control N°1" "Control N°2" "G 100µg/L"
## [6] "G 200µg/L" "P 1mg/L" "P 10mg/L"
yo<-sAll %>% count(cond,ID)
sAll$cond<-factor(sAll$cond,levels=c("Control N°1",
"A 200\u00b5g/L",
"A 500\u00b5g/L",
"Control N°2",
"G 100\u00b5g/L",
"G 200\u00b5g/L",
"P 1mg/L","P 10mg/L"))
levels(sAll$cond)
## [1] "Control N°1" "A 200µg/L" "A 500µg/L" "Control N°2" "G 100µg/L"
## [6] "G 200µg/L" "P 1mg/L" "P 10mg/L"
2) Create new variables
## Create second and min
T0<-sAll %>%
group_by(cond,ID) %>%
mutate(time=row_number()) %>%
mutate(second=time*0.4) %>%
mutate(min=second/60) %>%
ungroup()
T0$cat<-as.factor(T0$cat)
T0$ID<-as.factor(T0$ID)
T0$cond<-as.factor(T0$cond)
T0$pck<-as.factor(T0$pck)
T0$expe<-as.factor(T0$expe)
## Create average speed variable
TaG<-T0 %>%
mutate(sec=round(sec,digits=0)) %>%
group_by(cond,ID,sec) %>%
summarise(absdY=sum(absdY,na.rm=T))
Ta2<-TaG %>%
group_by(cond,ID) %>%
summarise(absdY=mean(absdY,na.rm=T))
m1<-glm(absdY~cond, data=Ta2)
ym1 <- predict(m1,type="link",se.fit=TRUE)
ydm1<-data.frame(ym1$fit,ym1$se.fit,Ta2$cond)
fm1<-ydm1 %>% rename(cond=Ta2.cond)
inm1<-family(m1)$linkinv
## Create Time spent moving by zone
dpos<-T0 %>% group_by(cond,ID) %>%
summarise(dpos=max(pos_y),
size=mean(size,na.rm=T),
pos_y=mean(pos_y))
dist<-mean(dpos$dpos,na.rm=T)
c<-dist/3
TZ<-T0 %>%
mutate(count=1) %>%
mutate(Zone = case_when(pos_y < c ~ "Top",
pos_y >= c & pos_y < 2*c ~ "Middle",
pos_y >= 2*c ~ "Bottom")) %>%
group_by(cond,Zone,ID) %>%
summarise(count=sum(count,na.rm=T),
absdY=mean(absdY,na.rm=T),
size=mean(size,na.rm=T),
pos_y=mean(pos_y)) %>% ungroup()
TZ1<-TZ %>%
group_by(cond,Zone) %>%
summarise(count=mean(count,na.rm=T),
absdY=mean(absdY,na.rm=T),
size=mean(size,na.rm=T),
pos_y=mean(pos_y))%>% ungroup()
TZA<-TZ1 %>%group_by(cond) %>% mutate(spend=100*count/sum(count)) %>% ungroup()
## Create % Time spent moving
lim<-1
casef<-T0 %>%
group_by(cond,ID) %>%
mutate(mov = case_when(absdY < lim ~ 0,
absdY >= lim ~ 1)) %>% ungroup()
iltf<-casef %>%
group_by(cond,ID) %>%
summarise(umov=sum(mov,na.rm=T),
n=n(),
tmov=(umov/n)*100,
sizem=max(size*45/400,na.rm=T),
usize=mean(size*45/400,na.rm=T),
pos_y=mean(pos_y)) %>% ungroup()
m2<-glm(tmov~cond, data=iltf)
ym2 <- predict(m2,type="link",se.fit=TRUE)
ydm2<-data.frame(ym2$fit,ym2$se.fit,iltf$cond)
fm2<-ydm2 %>% rename(cond=iltf.cond)
inm2<-family(m2)$linkinv
## Create number of diving events
TP<-T0 %>%
group_by(cond,ID) %>%
summarise(plong=mean(plong,na.rm=T))
m3<-glm(plong~cond, data=TP)
ym3 <- predict(m3,type="link",se.fit=TRUE)
ydm3<-data.frame(ym3$fit,ym3$se.fit,TP$cond)
fm3<-ydm3 %>% rename(cond=TP.cond)
inm3<-family(m3)$linkinv
rm(casef,dpos,m1,m2,m3,TaG,TZ,TZ1,ydm1,ydm2,ydm3,ym1,ym2,ym3)
3) Plot ATRAZINE –> Figure S1
Ta3<-Ta2 %>% filter(cond==c1|cond==c2|cond==c3)
fm11<-fm1 %>% filter(cond==c1|cond==c2|cond==c3)
a<-ggplot(Ta3,aes(x=cond,y=absdY,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm11,aes(x=cond,y=inm1(ym1.fit),
ymin=inm1(ym1.fit-1.96*ym1.se.fit),
ymax=inm1((ym1.fit+1.96*ym1.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
labs(y="Average speed (mm/sec)",x="")+
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#FFD966","#F0B33E","black"
,"black","black","black","black"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c3,c1),c(c3,c2)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TZA3<-TZA %>% filter(cond==c1|cond==c2|cond==c3)
b<-ggplot(TZA3)+
geom_point(aes(y=Zone,x=cond,size=spend,colour=cond))+
theme_classic() +
labs(y="Zones",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#FFD966","#F0B33E"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(strip.text = element_text(size = 20))+
scale_size(range = c(.1, 30), name="% Time spent")+
guides(color = FALSE)+
theme(legend.position='none')
iltf3<-iltf %>% filter(cond==c1|cond==c2|cond==c3)
fm21<-fm2 %>% filter(cond==c1|cond==c2|cond==c3)
c<-ggplot(iltf3,aes(x=cond,y=tmov,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm21,aes(x=cond,y=inm2(ym2.fit),
ymin=inm2(ym2.fit-1.96*ym2.se.fit),
ymax=inm2((ym2.fit+1.96*ym2.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Time spent moving (%)",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#FFD966","#F0B33E"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c3,c1),c(c3,c2)),
method.args = list(alternative = "greater"),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TP3<-TP %>% filter(cond==c1|cond==c2|cond==c3)
fm31<-fm3 %>% filter(cond==c1|cond==c2|cond==c3)
d<-ggplot(TP3,aes(x=cond,y=plong,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm31,aes(x=cond,y=inm3(ym3.fit),
ymin=inm3(ym3.fit-1.96*ym3.se.fit),
ymax=inm3((ym3.fit+1.96*ym3.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Diving events",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#FFD966","#F0B33E"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c3,c1),c(c3,c2)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
ggarrange(a,b,c,d,labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
4) Plot GLYPHOSATE –> Figure S2
Ta4<-Ta2 %>% filter(cond==c6|cond==c4|cond==c5)
fm12<-fm1 %>% filter(cond==c6|cond==c4|cond==c5)
a<-ggplot(Ta4,aes(x=cond,y=absdY,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm12,aes(x=cond,y=inm1(ym1.fit),
ymin=inm1(ym1.fit-1.96*ym1.se.fit),
ymax=inm1((ym1.fit+1.96*ym1.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
labs(y="Average speed (mm/sec)",x="")+
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#EB8176","#941100"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c5),c(c4,c6)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TZA4<-TZA %>% filter(cond==c6|cond==c4|cond==c5)
b<-ggplot(TZA4)+
geom_point(aes(y=Zone,x=cond,size=spend,colour=cond))+
theme_classic() +
labs(y="Zones",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#EB8176","#941100"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(strip.text = element_text(size = 20))+
scale_size(range = c(.1, 30), name="% Time spent")+
guides(color = FALSE)+
theme(legend.position='none')
iltf4<-iltf %>% filter(cond==c6|cond==c4|cond==c5)
fm22<-fm2 %>% filter(cond==c6|cond==c4|cond==c5)
c<-ggplot(iltf4,aes(x=cond,y=tmov,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm22,aes(x=cond,y=inm2(ym2.fit),
ymin=inm2(ym2.fit-1.96*ym2.se.fit),
ymax=inm2((ym2.fit+1.96*ym2.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Time spent moving (%)",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#EB8176","#941100"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c5),c(c4,c6)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TP4<-TP %>% filter(cond==c6|cond==c4|cond==c5)
fm32<-fm3 %>% filter(cond==c6|cond==c4|cond==c5)
d<-ggplot(TP4,aes(x=cond,y=plong,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm32,aes(x=cond,y=inm3(ym3.fit),
ymin=inm3(ym3.fit-1.96*ym3.se.fit),
ymax=inm3((ym3.fit+1.96*ym3.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Diving events",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#EB8176","#941100"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c5),c(c4,c6)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
ggarrange(a,b,c,d,labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
5) Plot PARACETAMOL –> Figure S3
Ta5<-Ta2 %>% filter(cond==c7|cond==c8|cond==c4)
fm13<-fm1 %>% filter(cond==c7|cond==c8|cond==c4)
a<-ggplot(Ta5,aes(x=cond,y=absdY,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm13,aes(x=cond,y=inm1(ym1.fit),
ymin=inm1(ym1.fit-1.96*ym1.se.fit),
ymax=inm1((ym1.fit+1.96*ym1.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
labs(y="Average speed (mm/sec)",x="")+
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#00A1EA","#0070C0"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c7),c(c4,c8)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TZA5<-TZA %>% filter(cond==c4|cond==c7|cond==c8)
b<-ggplot(TZA5)+
geom_point(aes(y=Zone,x=cond,size=spend,colour=cond))+
theme_classic() +
labs(y="Zones",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#00A1EA","#0070C0"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(strip.text = element_text(size = 20))+
scale_size(range = c(.1, 30), name="% Time spent")+
guides(color = FALSE)+
theme(legend.position='none')
iltf5<-iltf %>% filter(cond==c7|cond==c8|cond==c4)
fm23<-fm2 %>% filter(cond==c7|cond==c8|cond==c4)
c<-ggplot(iltf5,aes(x=cond,y=tmov,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm23,aes(x=cond,y=inm2(ym2.fit),
ymin=inm2(ym2.fit-1.96*ym2.se.fit),
ymax=inm2((ym2.fit+1.96*ym2.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Time spent moving (%)",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#00A1EA","#0070C0"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c7),c(c4,c8)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
TP5<-TP %>% filter(cond==c7|cond==c8|cond==c4)
fm33<-fm3 %>% filter(cond==c7|cond==c8|cond==c4)
d<-ggplot(TP5,aes(x=cond,y=plong,group=cond,color=cond))+
geom_point(alpha=0.8,size=2.5)+
geom_pointrange(data=fm33,aes(x=cond,y=inm3(ym3.fit),
ymin=inm3(ym3.fit-1.96*ym3.se.fit),
ymax=inm3((ym3.fit+1.96*ym3.se.fit)),
group=cond,color=cond),size=2,linewidth=3)+
theme_classic() +
labs(y="Diving events",x="")+
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.title = element_text(size=20,face = "bold"))+
theme(axis.text=element_text(size=20,color="black"),
axis.title=element_text(size=20,color="black"))+
scale_color_manual(values=c("#54C6CC","#00A1EA","#0070C0"))+
theme(legend.title = element_text(size=20),
legend.text = element_text(size=20))+
guides(colour=guide_legend(title="Species"))+
theme(legend.position = "none",
strip.text = element_text(size = 20))+
stat_compare_means(comparisons = list(
c(c4,c7),c(c4,c8)),
aes(label = ..p.signif..),
bracket.size = 1,size=5)
ggarrange(a,b,c,d,labels = c("A", "B", "C","D"),
ncol = 2, nrow = 2)
6) Stats –> Supp Table T4
mH1<-lm(absdY~cond,data=Ta3)
simH1 <- simulateResiduals(fittedModel = mH1, plot = T)
Anova(mH1)
## Anova Table (Type II tests)
##
## Response: absdY
## Sum Sq Df F value Pr(>F)
## cond 191.33 2 5.1255 0.007797 **
## Residuals 1679.81 90
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(mH1, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°1 9.24 0.741 90 7.77 10.7
## A 200µg/L 8.76 0.789 90 7.20 10.3
## A 500µg/L 12.08 0.802 90 10.49 13.7
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°1 - (A 200µg/L) 0.477 1.08 90 0.441 0.8985
## Control N°1 - (A 500µg/L) -2.843 1.09 90 -2.603 0.0288
## (A 200µg/L) - (A 500µg/L) -3.320 1.13 90 -2.951 0.0111
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH2<-lm(tmov~cond,data=iltf3)
simH2 <- simulateResiduals(fittedModel = mH2, plot = T)
Anova(mH2)
## Anova Table (Type II tests)
##
## Response: tmov
## Sum Sq Df F value Pr(>F)
## cond 1064.1 2 2.2964 0.1065
## Residuals 20852.4 90
emmeans(mH2, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°1 70.4 2.61 90 65.2 75.5
## A 200µg/L 67.2 2.78 90 61.7 72.7
## A 500µg/L 75.6 2.83 90 70.0 81.2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°1 - (A 200µg/L) 3.17 3.81 90 0.832 0.6841
## Control N°1 - (A 500µg/L) -5.24 3.85 90 -1.362 0.3650
## (A 200µg/L) - (A 500µg/L) -8.41 3.96 90 -2.123 0.0909
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH3<-lm(plong~cond,data=TP3)
simH3 <- simulateResiduals(fittedModel = mH3, plot = T)
Anova(mH3)
## Anova Table (Type II tests)
##
## Response: plong
## Sum Sq Df F value Pr(>F)
## cond 3408.9 2 5.4692 0.005733 **
## Residuals 28047.9 90
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(mH3, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°1 33.7 3.03 90 27.7 39.7
## A 200µg/L 32.2 3.22 90 25.8 38.6
## A 500µg/L 46.0 3.28 90 39.5 52.5
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°1 - (A 200µg/L) 1.51 4.42 90 0.341 0.9381
## Control N°1 - (A 500µg/L) -12.29 4.46 90 -2.755 0.0193
## (A 200µg/L) - (A 500µg/L) -13.80 4.60 90 -3.002 0.0096
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH1<-lm(absdY~cond,data=Ta4)
simH1 <- simulateResiduals(fittedModel = mH1, plot = T)
Anova(mH1)
## Anova Table (Type II tests)
##
## Response: absdY
## Sum Sq Df F value Pr(>F)
## cond 51.14 2 1.0889 0.3405
## Residuals 2371.83 101
emmeans(mH1, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 11.3 0.731 101 9.85 12.7
## G 100µg/L 12.0 0.885 101 10.22 13.7
## G 200µg/L 10.2 0.885 101 8.40 11.9
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (G 100µg/L) -0.678 1.15 101 -0.591 0.8254
## Control N°2 - (G 200µg/L) 1.144 1.15 101 0.997 0.5807
## (G 100µg/L) - (G 200µg/L) 1.821 1.25 101 1.456 0.3166
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH2<-lm(tmov~cond,data=iltf4)
simH2 <- simulateResiduals(fittedModel = mH2, plot = T)
Anova(mH2)
## Anova Table (Type II tests)
##
## Response: tmov
## Sum Sq Df F value Pr(>F)
## cond 146.6 2 0.6518 0.5233
## Residuals 11357.8 101
emmeans(mH2, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 74.8 1.60 101 71.6 78.0
## G 100µg/L 77.6 1.94 101 73.7 81.4
## G 200µg/L 75.4 1.94 101 71.5 79.2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (G 100µg/L) -2.804 2.51 101 -1.117 0.5059
## Control N°2 - (G 200µg/L) -0.594 2.51 101 -0.237 0.9696
## (G 100µg/L) - (G 200µg/L) 2.211 2.74 101 0.807 0.6993
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH3<-lm(plong~cond,data=TP4)
simH3 <- simulateResiduals(fittedModel = mH3, plot = T)
Anova(mH3)
## Anova Table (Type II tests)
##
## Response: plong
## Sum Sq Df F value Pr(>F)
## cond 1393 2 1.8479 0.1629
## Residuals 38055 101
emmeans(mH3, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 41.7 2.93 101 35.9 47.5
## G 100µg/L 46.4 3.54 101 39.4 53.4
## G 200µg/L 36.8 3.54 101 29.7 43.8
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (G 100µg/L) -4.67 4.60 101 -1.017 0.5681
## Control N°2 - (G 200µg/L) 4.96 4.60 101 1.079 0.5290
## (G 100µg/L) - (G 200µg/L) 9.63 5.01 101 1.922 0.1377
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH1<-lm(absdY~cond,data=Ta5)
simH1 <- simulateResiduals(fittedModel = mH1, plot = T)
Anova(mH1)
## Anova Table (Type II tests)
##
## Response: absdY
## Sum Sq Df F value Pr(>F)
## cond 32.22 2 0.7107 0.4937
## Residuals 2312.02 102
emmeans(mH1, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 11.3 0.718 102 9.87 12.7
## P 1mg/L 10.1 0.855 102 8.44 11.8
## P 10mg/L 10.2 0.869 102 8.50 11.9
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (P 1mg/L) 1.1644 1.12 102 1.043 0.5516
## Control N°2 - (P 10mg/L) 1.0753 1.13 102 0.954 0.6076
## (P 1mg/L) - (P 10mg/L) -0.0891 1.22 102 -0.073 0.9971
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH2<-lm(tmov~cond,data=iltf5)
simH2 <- simulateResiduals(fittedModel = mH2, plot = T)
Anova(mH2)
## Anova Table (Type II tests)
##
## Response: tmov
## Sum Sq Df F value Pr(>F)
## cond 156.1 2 0.5586 0.5737
## Residuals 14250.0 102
emmeans(mH2, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 74.8 1.78 102 71.3 78.3
## P 1mg/L 72.1 2.12 102 67.9 76.3
## P 10mg/L 74.8 2.16 102 70.5 79.1
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (P 1mg/L) 2.6541 2.77 102 0.958 0.6053
## Control N°2 - (P 10mg/L) -0.0458 2.80 102 -0.016 0.9999
## (P 1mg/L) - (P 10mg/L) -2.6998 3.03 102 -0.892 0.6467
##
## P value adjustment: tukey method for comparing a family of 3 estimates
mH3<-lm(plong~cond,data=TP5)
simH3 <- simulateResiduals(fittedModel = mH3, plot = T)
Anova(mH3)
## Anova Table (Type II tests)
##
## Response: plong
## Sum Sq Df F value Pr(>F)
## cond 511 2 0.733 0.483
## Residuals 35563 102
emmeans(mH3, pairwise ~ cond,adjust="tukey")
## $emmeans
## cond emmean SE df lower.CL upper.CL
## Control N°2 41.7 2.81 102 36.1 47.3
## P 1mg/L 37.4 3.35 102 30.8 44.1
## P 10mg/L 37.1 3.41 102 30.3 43.9
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Control N°2 - (P 1mg/L) 4.308 4.38 102 0.984 0.5887
## Control N°2 - (P 10mg/L) 4.627 4.42 102 1.047 0.5493
## (P 1mg/L) - (P 10mg/L) 0.319 4.78 102 0.067 0.9975
##
## P value adjustment: tukey method for comparing a family of 3 estimates